从 read_csv 中提取文件名 - Python

Question

I have a script that current reads raw data from a .csv file and performs some pandas data analysis against the data. Currently the .csv file is hardcoded and is read in like this:

data = pd.read_csv('test.csv',sep="|", names=col)

I want to change 2 things:

1. I want to turn this into a loop so it loops through a directory of .csv files and executes the pandas analysis below each one in the script.

2. I want to take each .csv file and strip the '.csv' and store that in a another list variable, let's call it 'new_table_list'.

I think I need something like below, at least for the 1st point(though I know this isn't completely correct). I am not sure how to address the 2nd point

Any help is appreciated

import os 

path = '\test\test\csvfiles'
table_list = []

for filename in os.listdir(path):
    if filename.endswith('.csv'):
        table_list.append(file)
data = pd.read_csv(table_list,sep="|", names=col)

Answer 1

Many ways to do it

for filename in os.listdir(path):
    if filename.endswith('.csv'):
        table_list.append(pd.read_csv(filename,sep="|"))
        new_table_list.append(filename.split(".")[0])

One more

for filename in os.listdir(path):
    if filename.endswith('.csv'):
        table_list.append(pd.read_csv(filename,sep="|"))
        new_table_list.append(filename[:-4])

and many more

As @barmar pointed out, better to append path as well to the table_list to avoid any issues related to path and location of files and script.

Answer 2

You can try something like this:

import glob

data = {}
for filename in glob.glob('/path/to/csvfiles/*.csv'):
    data[filename[:-4]] = pd.read_csv(filename, sep="|", names=col)

Then data.keys() is the list of filenames without the ".csv" part and data.values() is a list with one pandas dataframe for each file.

Answer 3

I'd start with using pathlib .

from pathlib import Path

And then leverage the stem attribute and glob method.

Let's make an import function.

def read_csv(f):
    return pd.read_csv(table_list, sep="|")

The most generic approach would be to store in a dictionary.

p = Path('\test\test\csvfiles')
dod = {f.stem: read_csv(f) for f in p.glob('*.csv')}

And you can also use pd.concat to turn that into a dataframe.

df = pd.concat(dod)

Answer 4

to get the list CSV files in the directory use glob it is easier than os

from glob import glob 

# csvs will contain all CSV files names ends with .csv in a list
csvs = glob('you\\dir\\to\\csvs_folder\\*.csv')

# remove the trailing .csv from CSV files names
new_table_list = [csv[:-3] for csv in csvs]

# read csvs as dataframes
dfs = [pd.read_csv(csv, sep="|", names=col) for csv in csvs]

#concatenate all dataframes into a single dataframe
df = pd.concat(dfs, ignore_index=True)

Answer 5

you can try so:

import os
path = 'your path'
all_csv_files = [f for f in os.listdir(path) if f.endswith('.csv')]
for f in all_csv_files:
    data = pd.read_csv(os.path.join(path, f), sep="|", names=col)

# list without .csv
files = [f[:-4] for f all_csv_files]

Answer 6

You can (at the moment of opening) add the filename to a Dataframe attribute as follow:

 ds.attrs['filename']='filename.csv'

You can subsequently query the dataframe for the name

 ds.attrs['filename']
'filename.csv'